Visualizing Data
PhD+ 2024: Data Literacy in R
About ggplot2
Developed by Hadley Wickham in 2005.
Implements the graphics scheme described in the book The Grammar of Graphics by Leland Wilkinson.
Uses a standardized system of syntax that makes it easy(-ish) to learn.
It takes care of a lot fiddly details such as colors, scales, and legend placement.
It does not do 3D or interactive graphics.
The Grammar of Graphics
The Grammar of Graphics boiled down to 5 bullets, courtesy of Wickham (2016, p. 4):
a statistical graphic is a mapping from data to aes thetic attributes (location, color, shape, size) of geom etric objects (points, lines, bars).
the geometric objects are drawn in a specific coord inate system.
scale s control the mapping from data to aesthetics and provide tools to read the plot (ie, axes and legends).
the plot may also contain stat istical transformations of the data (means, medians, bins of data, trend lines).
facet ing can be used to generate the same plot for different subsets of the data.
Basic ggplot2 syntax
Specify data, aesthetics and geometric shapes
ggplot(data, aes(x=, y=, color=, shape=, size=)) +geom_point(), or geom_histogram(), or geom_boxplot(), etc.
This combination is very effective for exploratory graphs.
The data must be a data frame.
The aes() function maps columns of the data frame to aesthetic properties of geometric shapes to be plotted.
ggplot() defines the plot; the geoms show the data; each component is added with +
Some examples should make this clear
The Albemarle county homes data
We’ll demonstrate ggplot2 using the Albemarle County real estate data, which was downloaded from Office of Geographic Data Services.
Some variables of interest:
total value of home (totalvalue)
finished square feet (finsqft)
high school district in which house is located (hsdistrict)
Note: the following examples use a sample of the homes data.
scatter plot colored by high school district
library (ggplot2) # or library(tidyverse) ggplot (homes, aes (x= finsqft, y= totalvalue, color= hsdistrict)) + geom_point ()
add multiple geoms (points and smooth line)
ggplot (homes, aes (x= finsqft, y= totalvalue, color= hsdistrict)) + geom_point () + geom_smooth ()
Moving beyond ggplot + geoms
A natural next step in exploratory graphing is to create plots of subsets of data. These are called facets in ggplot2.
Use facet_wrap() if you want to facet by one variable and have ggplot2 control the layout. Example:
Use facet_grid() if you want to facet by one and/or two variables and control layout yourself.
Examples:facet_grid(. ~ var1) - facets in columnsfacet_grid(var1 ~ .) - facets in rowsfacet_grid(var1 ~ var2) - facets in rows and columns
facet_wrap
ggplot (homes, aes (x= finsqft, y= totalvalue)) + geom_point () + facet_wrap (~ hsdistrict)
facet_grid (histograms)
ggplot (homes, aes (x= finsqft, y = after_stat (density))) + geom_histogram () + facet_grid (hsdistrict ~ .)
Modifying the coordinate system
coord_cartesian allows us to zoom in on a plot, as if using magnifying glasscoord_fixed allows us to control “aspect ratio”coord_flip allows us to flip the x and y axis
zoom in on plot
ggplot (homes, aes (x= finsqft, y= totalvalue, color= hsdistrict)) + geom_point () + coord_cartesian (ylim = c (1e5 , 3e5 ), xlim = c (0 ,3000 ))
Customizing scales
Scales control the mapping from data to aesthetics and provide tools to read the plot (ie, axes and legends).
Every aesthetic has a default scale. To modify a scale, use a scale function.
All scale functions have a common naming scheme:scale _ name of aesthetic _ name of scale
Examples: scale_y_continuous, scale_color_discrete, scale_fill_manual
Heads up: The documentation for ggplot2 scale functions will frequently use functions from the scales package (also by Wickham)!
update scales for x- and y-axis
ggplot (homes, aes (x= finsqft, y= totalvalue, color= hsdistrict)) + geom_point () + scale_y_continuous (labels = scales:: label_currency ()) + scale_x_continuous (labels = scales:: label_comma ())
update scales for color
ggplot (homes, aes (x= finsqft, y= totalvalue, color= hsdistrict)) + geom_point () + scale_y_continuous (labels = scales:: label_currency ()) + scale_x_continuous (labels = scales:: label_comma ()) + scale_color_brewer ("HS District" , palette = "Set2" ) # colorblind friendly palette
Update themes and labels
The default ggplot2 theme is excellent. It follows the advice of several landmark papers regarding statistics and visual perception. (Wickham 2016, p. 176)
However you can change the theme using ggplot2’s themeing system. To date, there are seven built-in themes: theme_gray (default ), theme_bw, theme_linedraw, theme_light, theme_dark, theme_minimal, theme_classic
You can also update axis labels and titles using the labs function.
change theme
ggplot (homes, aes (x= finsqft, y= totalvalue, color = hsdistrict)) + geom_point () + theme_minimal ()
update labels
ggplot (homes, aes (x= finsqft, y= totalvalue, color = hsdistrict)) + geom_point () + labs (title= "Total Value versus Finished Square Feet" , x= "Finished Square Feet" , y= "Total Value (USD)" , color = "HS District" )
Making plots interactive
Using the ggplotly() function from the {plotly} package, we can make (some) plots interactive.
library (plotly)<- ggplot (homes, aes (x= finsqft, y= totalvalue, color = hsdistrict)) + geom_point () + labs (title= "Total Value versus Finished Square Feet" , x= "Finished Square Feet" , y= "Total Value (USD)" ) ggplotly (p)
ggplot2 - some tips
Can do a lot with ggplot(data, aes()) + geom!
Data must be a data frame (not a matrix or collection of vectors)
The ggplot2 documentation has many good examples
Prepare to invest some time if you want master ggplot2; the RStudio ggplot2 cheat sheet can help.
References
More on plotly
